SoroushVahidi/scidocs huggingface dataset:
A low-power capacitor switching scheme with low common-mode voltage variation for successive approximation ADC
(2017)

In this paper, a new low-energy switching technique with low common-mode voltage variation is proposed for successive-approximation analog-to-digital converters (SA-ADCs). In the proposed scheme, not only the switching energy consumed within the first three comparisons is less than zero, but also other comparisons are made with the low-power monotonic method. Therefore, the switching energy of the capacitive array, including the consumed energy during the sampling phase, is reduced by 90.68% compared with the conventional counterpart. Moreover, since the variation of the input common-mode voltage of the employed comparator is only 0.125Vref, where Vref is the reference voltage of the ADC, the required comparator’s performance can be much more relaxed leading to more power saving. Post-layout simulation results of a 10-bit 1-MS/s SA-ADC in a 0.18-μm CMOS technology show a signal-to-noise-and-distortion ratio (SNDR) of 61 dB, a spurious-free dynamic range (SFDR) of 79.8 dB, and an effective number of 9.84 bits. The ADC consumes 35.3 μW with a 1.8-V supply and achieves a Figure-of-Merit (FoM) of 38.5 fJ/conversion-step.
A comprehensive and conservative approach for the restoration of abrasion and erosion. Part I: concepts and clinical rationale for early intervention using adhesive techniques.
(2011)

Tooth wear represents a frequent pathology with multifactorial origins. Behavioral changes, unbalanced diet, various medical conditions and medications inducing acid regurgitation or influencing saliva composition and flow rate, trigger tooth erosion. Awake and sleep bruxism, which are widespread nowadays with functional disorders, induce attrition. It has become increasingly important to diagnose early signs of tooth wear so that proper preventive, and if needed, restorative measures are taken. Such disorders have biological, functional, and also esthetic consequences. Following a comprehensive clinical evaluation, treatment objectives, such as a proper occlusal and anatomical scheme as well as a pleasing smile line, are usually set on models with an anterior teeth full-mouth waxup, depending on the severity of tissue loss. Based on the new vertical dimension of occlusion (VDO), combinations of direct and indirect restorations can then help to reestablish anatomy and function. The use of adhesive techniques and resin composites has demonstrated its potential, in particular for the treatment of moderate tooth wear. Part I of this article reviews recent knowledge and clinical concepts dealing with the various forms of early restorative interventions and their potential to restrict ongoing tissue destruction.
Kernel Clustering: Density Biases and Solutions
(2019)

Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the density mode isolation bias in kernel K-means for a common class of kernels. We call it Breiman’s bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity. We also extend our analysis to other popular kernel clustering methods, e.g., average/normalized cut or dominant sets, where density biases can take different forms. For example, splitting isolated points by cut-based criteria is essentially the sparsest subset bias, which is the opposite of the density mode bias. Our findings suggest that a principled solution for density biases in kernel clustering should directly address data inhomogeneity. We show that density equalization can be implicitly achieved using either locally adaptive weights or locally adaptive kernels. Moreover, density equalization makes many popular kernel clustering objectives equivalent. Our synthetic and real data experiments illustrate density biases and proposed solutions. We anticipate that theoretical understanding of kernel clustering limitations and their principled solutions will be important for a broad spectrum of data analysis applications across the disciplines.
A Framework for Validating Models of Evasion Attacks on Machine Learning, with Application to PDF Malware Detection
(2017)

Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, there is increasing evidence that machine learning models are susceptible to evasion attacks, in which an adversary makes small changes to the input (such as malware) in order to cause erroneous predictions (for example, to avoid being detected). Evasion attacks on ML fall into two broad categories: 1) those which generate actual malicious instances and demonstrate both evasion of ML and efficacy of attack (we call these problem space attacks), and 2) attacks which directly manipulate features used by ML, abstracting efficacy of attack into a mathematical cost function (we call these feature space attacks). Central to our inquiry is the following fundamental question: are feature space models of attacks useful proxies for real attacks? In the process of answering this question, we make two major contributions: 1) a general methodology for evaluating validity of mathematical models of ML evasion attacks, and 2) an application of this methodology as a systematic hypothesis-driven evaluation of feature space evasion attacks on ML-based PDF malware detectors. Specific to our case study, we find that a) feature space evasion models are in general not adequate in representing real attacks, b) such models can be significantly improved by identifying conserved features (features that are invariant in real attacks) whenever these exist, and c) ML hardened using the improved feature space models remains robust to alternative attacks, in contrast to ML hardened using a very powerful class of problem space attacks, which does not.
Vitamin D: An overview of vitamin D status and intake in Europe
(2014)

In recent years, there have been reports suggesting a high prevalence of low vitamin D intakes and vitamin D deficiency or inadequate vitamin D status in Europe. Coupled with growing concern about the health risks associated with low vitamin D status, this has resulted in increased interest in the topic of vitamin D from healthcare professionals, the media and the public. Adequate vitamin D status has a key role in skeletal health. Prevention of the well-described vitamin D deficiency disorders of rickets and osteomalacia are clearly important, but there may also be an implication of low vitamin D status in bone loss, muscle weakness and falls and fragility fractures in older people, and these are highly significant public health issues in terms of morbidity, quality of life and costs to health services in Europe. Although there is no agreement on optimal plasma levels of vitamin D, it is apparent that blood 25-hydroxyvitamin D [25(OH)D] levels are often below recommended ranges for the general population and are particularly low in some subgroups of the population, such as those in institutions or who are housebound and non-Western immigrants. Reported estimates of vitamin D status within different European countries show large variation. However, comparison of studies across Europe is limited by their use of different methodologies. The prevalence of vitamin D deficiency [often defined as plasma 25(OH)D <25 nmol/l] may be more common in populations with a higher proportion of at-risk groups, and/or that have low consumption of foods rich in vitamin D (naturally rich or fortified) and low use of vitamin D supplements. The definition of an adequate or optimal vitamin D status is key in determining recommendations for a vitamin D intake that will enable satisfactory status to be maintained all year round, including the winter months. In most European countries, there seems to be a shortfall in achieving current vitamin D recommendations. An exception is Finland, where dietary survey data indicate that recent national policies that include fortification and supplementation, coupled with a high habitual intake of oil-rich fish, have resulted in an increase in vitamin D intakes, but this may not be a suitable strategy for all European populations. The ongoing standardisation of measurements in vitamin D research will facilitate a stronger evidence base on which policies can be determined. These policies may include promotion of dietary recommendations, food fortification, vitamin D supplementation and judicious sun exposure, but should take into account national, cultural and dietary habits. For European nations with supplementation policies, it is important that relevant parties ensure satisfactory uptake of these particularly in the most vulnerable groups of the population.
A Survey of Incentive Mechanisms for Participatory Sensing
(2015)

Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004-2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.
Transferring sentiment knowledge between words and tweets
(2018)

Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.
The Fog computing paradigm: Scenarios and security issues
(2014)

Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.
IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems
(2017)

The Internet of Things (IoT) is entering the daily operation of many industries; applications include but are not limited to smart cities, smart grids, smart homes, physical security, e-health, asset management, and logistics. For example, the concept of smart cities is emerging in multiple continents, where enhanced street lighting controls, infrastructure monitoring, public safety and surveillance, physical security, gunshot detection, meter reading, and transportation analysis and optimization systems are being deployed on a city-wide scale. A related and cost-effective user-level IoT application is the support of IoT-enabled smart buildings. Commercial space has substantial requirements in terms of comfort, usability, security, and energy management. IoT-based systems can support these requirements in an organic manner. In particular, power over Ethernet, as part of an IoT-based solution, offers disruptive opportunities in revolutionizing the in-building connectivity of a large swath of devices. However, a number of deployment-limiting issues currently impact the scope of IoT utilization, including lack of comprehensive end-to-end standards, fragmented cybersecurity solutions, and a relative dearth of fully-developed vertical applications. This paper reviews some of the technical opportunities offered and the technical challenges faced by the IoT in the smart building arena.
Cyber security in the Smart Grid: Survey and challenges
(2013)

The Smart Grid, generally referred to as the next-generation power system, is considered as a revolutionary and evolutionary regime of existing power grids. More importantly, with the integration of advanced computing and communication technologies, the Smart Grid is expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response. Along with the silent features of the Smart Grid, cyber security emerges to be a critical issue because millions of electronic devices are inter-connected via communication networks throughout critical power facilities, which has an immediate impact on reliability of such a widespread infrastructure. In this paper, we present a comprehensive survey of cyber security issues for the Smart Grid. Specifically, we focus on reviewing and discussing security requirements, network vulnerabilities, attack countermeasures, secure communication protocols and architectures in the Smart Grid. We aim to provide a deep understanding of security vulnerabilities and solutions in the Smart Grid and shed light on future research directions for Smart Grid security. 2013 Elsevier B.V. All rights reserved.
EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications
(2012)

The concept of smart grid has emerged as a convergence of traditional power system engineering and information and communication technology. It is vital to the success of next generation of power grid, which is expected to be featuring reliable, efficient, flexible, clean, friendly, and secure characteristics. In this paper, we propose an efficient and privacy-preserving aggregation scheme, named EPPA, for smart grid communications. EPPA uses a superincreasing sequence to structure multidimensional data and encrypt the structured data by the homomorphic Paillier cryptosystem technique. For data communications from user to smart grid operation center, data aggregation is performed directly on ciphertext at local gateways without decryption, and the aggregation result of the original data can be obtained at the operation center. EPPA also adopts the batch verification technique to reduce authentication cost. Through extensive analysis, we demonstrate that EPPA resists various security threats and preserve user privacy, and has significantly less computation and communication overhead than existing competing approaches.
MigCEP: operator migration for mobility driven distributed complex event processing
(2013)

A recent trend in communication networks --- sometimes referred to as fog computing --- offers to execute computational tasks close to the access points of the networks. This enables real-time applications, like mobile Complex Event Processing (CEP), to significantly reduce end-to-end latencies and bandwidth usage. Most work studying the placement of operators in such an environment completely disregards the migration costs. However, the mobility of users requires frequent migration of operators, together with possibly large state information, to meet latency restrictions and save bandwidth in the infrastructure.
 This paper presents a placement and migration method for providers of infrastructures that incorporate cloud and fog resources. It ensures application-defined end-to-end latency restrictions and reduces the network utilization by planning the migration ahead of time. Furthermore, we present how the application knowledge of the CEP system can be used to improve current live migration techniques for Virtual Machines to reduce the required bandwidth during the migration. Our evaluations show that we safe up to 49% of the network utilization with perfect knowledge about a users mobility pattern and up to 27% of the network utilization when considering the uncertainty of those patterns.
Beyond PASCAL: A benchmark for 3D object detection in the wild
(2014)

3D object detection and pose estimation methods have become popular in recent years since they can handle ambiguities in 2D images and also provide a richer description for objects compared to 2D object detectors. However, most of the datasets for 3D recognition are limited to a small amount of images per category or are captured in controlled environments. In this paper, we contribute PASCAL3D+ dataset, which is a novel and challenging dataset for 3D object detection and pose estimation. PASCAL3D+ augments 12 rigid categories of the PASCAL VOC 2012 [4] with 3D annotations. Furthermore, more images are added for each category from ImageNet [3]. PASCAL3D+ images exhibit much more variability compared to the existing 3D datasets, and on average there are more than 3,000 object instances per category. We believe this dataset will provide a rich testbed to study 3D detection and pose estimation and will help to significantly push forward research in this area. We provide the results of variations of DPM [6] on our new dataset for object detection and viewpoint estimation in different scenarios, which can be used as baselines for the community. Our benchmark is available online at http://cvgl.stanford.edu/projects/pascal3d.
Improving retention and graduate recruitment through immersive research experiences for undergraduates
(2008)

Research experiences for undergraduates are considered an effective means for increasing student retention and encouraging undergraduate students to continue on to graduate school. However, managing a cohort of undergraduate researchers, with varying skill levels, can be daunting for faculty advisors. We have developed a program to engage students in research and outreach in visualization, virtual reality, networked robotics, and interactive games. Our program immerses students into the life of a lab, employing a situated learning approach that includes tiered mentoring and collaboration to enable students at all levels to contribute to research. Students work in research teams comprised of other undergraduates, graduate students and faculty, and participate in professional development and social gatherings within the larger cohort. Results from our first two years indicate this approach is manageable and effective for increasing students' ability and desire to conduct research.
Silicon carbide Schottky diodes and MOSFETs: Solutions to performance problems
(2008)

Silicon carbide has long been hailed as the successor to silicon in many power electronics applications. Its superior electrical and thermal properties have delivered devices that operate at higher voltages, higher temperatures and with lower on-resistances than silicon devices. However, SiC Schottky diodes are still the only devices commercially available today. Though SiC Schottkys are now being used with silicon IGBTs in dasiahybridpsila inverter modules, the real advantages will be seen when silicon switching devices can be replaced by SiC. This paper describes the current state of SiC diode and MOSFET technology, discussing possible solutions to making these devices commercially viable.
How do software engineers understand code changes?: an exploratory study in industry
(2012)

Software evolves with continuous source-code changes. These code changes usually need to be understood by software engineers when performing their daily development and maintenance tasks. However, despite its high importance, such change-understanding practice has not been systematically studied. Such lack of empirical knowledge hinders attempts to evaluate this fundamental practice and improve the corresponding tool support.
 To address this issue, in this paper, we present a large-scale quantitative and qualitative study at Microsoft. The study investigates the role of understanding code changes during software-development process, explores engineers' information needs for understanding changes and their requirements for the corresponding tool support. The study results reinforce our beliefs that understanding code changes is an indispensable task performed by engineers in software-development process. A number of insufficiencies in the current practice also emerge from the study results. For example, it is difficult to acquire important information needs such as a change's completeness, consistency, and especially the risk imposed by it on other software components. In addition, for understanding a composite change, it is valuable to decompose it into sub-changes that are aligned with individual development issues; however, currently such decomposition lacks tool support.
Text Coherence Analysis Based on Deep Neural Network
(2017)

In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.